






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class        a       (					lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					int							=      KandinskyVaaInpaintPipeline
      a    :					Any							=      ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
      a    :					Any							=      [
          'image_embeds',
          'negative_image_embeds',
          'image',
          'mask_image',
      ]
      a    :					Any							=      [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      a    :					List[Any]							=      False
      @property
      def       UpperCAmelCase ( self   :     int   )				-> Dict:
        return 32
      @property
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[Any]:
        return 32
      @property
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Optional[int]:
        return self.time_input_dim
      @property
      def       UpperCAmelCase ( self   :     str   )				-> List[str]:
        return self.time_input_dim * 4
      @property
      def       UpperCAmelCase ( self   :     Tuple   )				-> List[str]:
        return 100
      @property
      def       UpperCAmelCase ( self   :     Dict   )				-> Any:
        torch.manual_seed(0   )

        __UpperCAmelCase				:      Optional[Any]	 =    {
            """in_channels""": 9,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        __UpperCAmelCase				:      int	 =    UNetaDConditionModel(**__lowercase   )
        return model
      @property
      def       UpperCAmelCase ( self   :     int   )				-> int:
        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }
      @property
      def       UpperCAmelCase ( self   :     Dict   )				-> List[str]:
        torch.manual_seed(0   )
        __UpperCAmelCase				:      List[Any]	 =    VQModel(**self.dummy_movq_kwargs   )
        return model
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        __UpperCAmelCase				:      List[str]	 =    self.dummy_unet
        __UpperCAmelCase				:      List[str]	 =    self.dummy_movq

        __UpperCAmelCase				:      Optional[Any]	 =    DDIMScheduler(
            num_train_timesteps=1000						, beta_schedule="""linear"""						, beta_start=0.00_085						, beta_end=0.012						, clip_sample=__lowercase						, set_alpha_to_one=__lowercase						, steps_offset=1						, prediction_type="""epsilon"""						, thresholding=__lowercase						, )

        __UpperCAmelCase				:      str	 =    {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     Tuple						, __lowercase   :     List[str]=0   )				-> Optional[Any]:
        __UpperCAmelCase				:      str	 =    floats_tensor((1, self.text_embedder_hidden_size)						, rng=random.Random(__lowercase   )   ).to(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    floats_tensor((1, self.text_embedder_hidden_size)						, rng=random.Random(seed + 1   )   ).to(
            __lowercase   )
        # create init_image
        __UpperCAmelCase				:      Tuple	 =    floats_tensor((1, 3, 64, 64)						, rng=random.Random(__lowercase   )   ).to(__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    image.cpu().permute(0						, 2						, 3						, 1   )[0]
        __UpperCAmelCase				:      Union[str, Any]	 =    Image.fromarray(np.uinta(__lowercase   )   ).convert("""RGB"""   ).resize((256, 256)   )
        # create mask
        __UpperCAmelCase				:      Union[str, Any]	 =    np.ones((64, 64)						, dtype=np.floataa   )
        __UpperCAmelCase				:      List[str]	 =    0

        if str(__lowercase   ).startswith("""mps"""   ):
          __UpperCAmelCase				:      List[str]	 =    torch.manual_seed(__lowercase   )
        else:
          __UpperCAmelCase				:      Optional[int]	 =    torch.Generator(device=__lowercase   ).manual_seed(__lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    {
            """image""": init_image,
            """mask_image""": mask,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 2,
            """guidance_scale""": 4.0,
            """output_type""": """np""",
        }
        return inputs
      def       UpperCAmelCase ( self   :     List[str]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Optional[Any]	 =    """cpu"""

        __UpperCAmelCase				:      Dict	 =    self.get_dummy_components()

        __UpperCAmelCase				:      str	 =    self.pipeline_class(**__lowercase   )
        __UpperCAmelCase				:      Tuple	 =    pipe.to(__lowercase   )

        pipe.set_progress_bar_config(disable=__lowercase   )

        __UpperCAmelCase				:      List[Any]	 =    pipe(**self.get_dummy_inputs(__lowercase   )   )
        __UpperCAmelCase				:      Tuple	 =    output.images

        __UpperCAmelCase				:      Optional[int]	 =    pipe(
            **self.get_dummy_inputs(__lowercase   )						, return_dict=__lowercase						, )[0]

        __UpperCAmelCase				:      Union[str, Any]	 =    image[0, -3:, -3:, -1]
        __UpperCAmelCase				:      str	 =    image_from_tuple[0, -3:, -3:, -1]

        print(f"""image.shape {image.shape}"""   )

        assert image.shape == (1, 64, 64, 3)

        __UpperCAmelCase				:      Optional[Any]	 =    np.array(
            [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]   )

        assert (
            np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
        ), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
        ), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




      def       UpperCAmelCase ( self   :     str   )				-> Union[str, Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3   )


@slow
@require_torch_gpu
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




      def       UpperCAmelCase ( self   :     Tuple   )				-> Union[str, Any]:
        __UpperCAmelCase				:      Any	 =    load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""   )

        __UpperCAmelCase				:      List[str]	 =    load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""   )
        __UpperCAmelCase				:      List[Any]	 =    np.ones((768, 768)						, dtype=np.floataa   )
        __UpperCAmelCase				:      Optional[Any]	 =    0

        __UpperCAmelCase				:      Tuple	 =    """a hat"""

        __UpperCAmelCase				:      str	 =    KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""						, torch_dtype=torch.floataa   )
        pipe_prior.to(__lowercase   )

        __UpperCAmelCase				:      Any	 =    KandinskyVaaInpaintPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-decoder-inpaint"""						, torch_dtype=torch.floataa   )
        __UpperCAmelCase				:      int	 =    pipeline.to(__lowercase   )
        pipeline.set_progress_bar_config(disable=__lowercase   )

        __UpperCAmelCase				:      int	 =    torch.Generator(device="""cpu"""   ).manual_seed(0   )
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    pipe_prior(
            __lowercase						, generator=__lowercase						, num_inference_steps=5						, negative_prompt=""""""						, ).to_tuple()

        __UpperCAmelCase				:      Optional[int]	 =    pipeline(
            image=__lowercase						, mask_image=__lowercase						, image_embeds=__lowercase						, negative_image_embeds=__lowercase						, generator=__lowercase						, num_inference_steps=100						, height=768						, width=768						, output_type="""np"""						, )

        __UpperCAmelCase				:      List[Any]	 =    output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase						, __lowercase   )







'''simple docstring'''






import os

try:
     from .build_directory_md import good_file_paths
except ImportError:
     from build_directory_md import good_file_paths  # type: ignore

lowerCAmelCase:   Optional[int]					=		list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

lowerCAmelCase:   Optional[Any]					=		[file for file in filepaths if file != file.lower()]
if upper_files:
     print(F"{len(upper_files)} files contain uppercase characters:")
     print('\n'.join(upper_files) + '\n')

lowerCAmelCase:   int					=		[file for file in filepaths if ' ' in file]
if space_files:
     print(F"{len(space_files)} files contain space characters:")
     print('\n'.join(space_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if '-' in file]
if hyphen_files:
     print(F"{len(hyphen_files)} files contain hyphen characters:")
     print('\n'.join(hyphen_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if os.sep not in file]
if nodir_files:
     print(F"{len(nodir_files)} files are not in a directory:")
     print('\n'.join(nodir_files) + '\n')

lowerCAmelCase:   int					=		len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
     import sys

     sys.exit(bad_files)